213 Genomic selection of carcass quality traits in crossbred pigs using a reference population

Abstract

Abstract The objective of this work was to evaluate the predictive ability of different models applied to carcass traits in crossbred pigs. The pigs were divided in 2 finishing flows: A=36,110 and B=95,041 animals, and were progeny of 386 sires (almost entirely genotyped with the 60k SNP chip). In flow A, individuals were housed into single-sire single-gender pens, and split-marketing on a pen basis was applied. In flow B, individuals were kept in standard commercial conditions and split-marketing on an individuals basis was applied. A dataset containing individual records of three carcass traits: back-fat (BF), loin depth (LD), and carcass daily gain (CACG) was used. Data from flow A were divided into training and validation sets on the basis of contemporary groups (8 in training and 1 in testing). Variance components and solutions were obtained using the BLUPF90 suite of programs. Models included fixed effects (dam line, sow parity, sex, cross fostering, and contemporary group) and random effects (additive genetic, batch, litter, and residual). Models tested were univariate vs multivariate and pedigree vs single-step. The addition of flow B records to the training set was evaluated, by including or excluding these records. Heritabilities were 0.68±0.023 for BF, 0.47±0.018 for LD, and 0.55±0.023 for CACG. CACG gain was correlated with BF (0.43±0.029) and LD (0.39±0.03). Low genetic correlation was found between BF and LD (0.17±0.034). Prediction accuracies were 0.39±0.05, 0.17±0.06, and 0.13±0.03 for BF, LD, and CACG respectively. The mean accuracy of BF, LD, and CG increased (~6%) when records from flow B were included in the training set, whereas the increase of accuracy between models (univariate vs multivariate) was not significant. The inclusion of sire genotypes did not improve prediction accuracy significantly. Based on these results, the prediction of carcass quality traits in crossbred pigs is possible.